Conclusion - Laser-engraved Microfluidics for Iontophoresis-induced Sweat

Chapter 4: Laser-engraved Microfluidics for Sweat Sampling

4.3 Laser-engraved Microfluidics for Iontophoresis-induced Sweat

4.3.3 Conclusion

Figure 4-13. Characterization of continuous microfluidic sensing performance under different flow rates. a, The DPV voltammograms of an LEG Trp sensor at different flow rates (from 0.15 to 3 μL min^-1). b, The peak height current densities of the Trp sensor in 3 successive DPV scans under each flow rate. c,d, DPV voltammograms of an LEG Trp sensor in three repetitive scans at a flow rate of 0.2 μL min^-1 (c) and 0.5 μL min^-1 (d), respectively. Conditions, one scan every 2.5 min in 40 μM Trp.

Bibliography of Chapter 4

1. Baker, L. B. Physiology of sweat gland function: The roles of sweating and sweat composition in human health. Temperature 6, 211–259 (2019).

2. Harshman, S. W. et al. Metabolomic stability of exercise-induced sweat. Journal of Chromatography B 1126–1127, 121763 (2019).

3. Harshman, S. W. et al. The proteomic and metabolomic characterization of exercise-induced sweat for human performance monitoring: A pilot investigation.

Plos One 13, e0203133 (2018).

4. Heikenfeld, J. et al. Accessing analytes in biofluids for peripheral biochemical monitoring. Nature Biotechnology 37, 407–419 (2019).

5. Ray, T. R. et al. Soft, skin-interfaced sweat stickers for cystic fibrosis diagnosis and management. Science Translational Medicine 13, eabd8109 (2021).

6. Choi, J., Kang, D., Han, S., Kim, S. B. & Rogers, J. A. Thin, soft, skin-mounted microfluidic networks with capillary bursting valves for chrono-sampling of sweat. Advanced Healthcare Materials 6, 1601355 (2017).

7. Agrawal, K., Waller, J. D., Pedersen, T. L. & Newman, J. W. Effects of stimulation technique, anatomical region, and time on human sweat lipid mediator profiles. Prostaglandins & Other Lipid Mediators 134, 84–92 (2018).

8. Bariya, M., Nyein, H. Y. Y. & Javey, A. Wearable sweat sensors. Nature Electronics 1, 160–171 (2018).

9. Sonner, Z., Wilder, E., Gaillard, T., Kasting, G. & Heikenfeld, J. Integrated sudomotor axon reflex sweat stimulation for continuous sweat analyte analysis with individuals at rest. Lab on a Chip 17, 2550–2560 (2017).

10. Sempionatto, J. R. et al. An epidermal patch for the simultaneous monitoring of haemodynamic and metabolic biomarkers. Nature Biomedical Engineering 5, 737–748 (2021).

11. Emaminejad, S. et al. Autonomous sweat extraction and analysis applied to cystic fibrosis and glucose monitoring using a fully integrated wearable platform.

Proceedings of the National Academy of Sciences of the United States of America 114, 4625–4630 (2017).

12. Kim, J. et al. Noninvasive alcohol monitoring using a wearable tattoo-based iontophoretic-biosensing system. ACS Sensors 1, 1011–1019 (2016).

13. Choi, J., Ghaffari, R., Baker, L. B. & Rogers, J. A. Skin-interfaced systems for sweat collection and analytics. Science Advances 4, eaar3921 (2018).

14. Ma, Y., Zhu, C., Ma, P. & Yu, K. T. Studies on the diffusion coefficients of amino acids in aqueous solutions. Journal of Chemical & Engineering Data 50, 1192–1196 (2005).

15. Low, P. A., Opfer-Gehrking, T. L. & Kihara, M. In vivo studies on receptor pharmacology of the human eccrine sweat gland. Clinical Autonomic Research 2, 29–34 (1992).

16. Simmers, P., Li, S. K., Kasting, G. & Heikenfeld, J. Prolonged and localized sweat stimulation by iontophoretic delivery of the slowly-metabolized cholinergic agent carbachol. Journal of Dermatological Science 89, 40–51 (2018).

17. Low, P. A., Caskey, P. E., Tuck, R. R., Fealey, R. D. & Dyck, P. J. Quantitative sudomotor axon reflex test in normal and neuropathic subjects. Annals of Neurology 14, 573–580 (1983).

18. Aromdee, C., Fawcett, J. P., Ferguson, M. M. & Ledger, R. Serum pilocarpine esterase activity and response to oral pilocarpine. Biochemical and Molecular Medicine 59, 57–61 (1996).

19. Riedl, B., Nischik, M., Birklein, F., Neundörfer, B. & Handwerker, H. O. Spatial extension of sudomotor axon reflex sweating in human skin. Journal of the Autonomic Nervous System 69, 83–88 (1998).

A p p e n d i x C

SUPPLEMENTARY INFORMATION FOR CHAPTER 4

Materials from this chapter appears in “Yang, Y.; Song, Y.; Bo, X.; Min, J.; Pak, O.

S.; Zhu, L.; Wang, M.; Tu, J.; Kogan, A.; Zhang, H.; Hsiai, T. K.; Li, Z.; Gao, W. A laser-engraved wearable sensor for sensitive detection of uric acid and tyrosine in sweat. Nature biotechnology 38, 217–224 (2020) doi:10.1038/s41587-019-0321-x”

and “Wang, M.; Yang, Y.; Min, J.; Song, Y.; Tu, J.; Mukasa, D.; Ye, C.; Xu, C.;

Heflin, N.; McCune, J. S.; Hsiai, T. K.; Li, Z.; Gao, W. A wearable electrochemical biosensor for the monitoring of metabolites and nutrients. Nature Biomedical Engineering 1–11 (2022) doi: 10.1038/s41551-022-00916-z”.

Figure C-1. Fabrication process of the microfluidic patch

Figure C-2. Microscopic images showing the resolution of the laser engraving. a–

d, a graphene microelectrode fabricated by the raster mode (a), a graphene micropattern fabricated

by vector mode (b), and a microfluidic channel fabricated by vector mode under top view (c) and

cross-sectional view (d). Scale bars, 100 μm

Medical tape Laser-cut microfluidics PET attachment Another medical tape

PI cleaning Vector mode laser cutting Raster mode laser cutting Multimodal sensing patch

PI Chemical sensor Strain sensor Temperature sensor Medical tape PET

a b

d c

Figure C-3. The numerical simulation showing the fluidic dynamics in the reservoir of the lab-on-skin sensor patch. The dimension used here are based on actual sensor design used in this work. Scale bar, 5 mm.

Figure C-4. Fabrication process of the multifunctional flexible wearable sensor patch.

30 s 60 s 120 s 180 s 240 s

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Medical tape – accumulation layer

Supplementary Fig. 1. Fabrication process of the wearable patch.

PI cleaning Laser-engraved graphene Sensor preparation NutriTrek sensor patch

Medical tape – reservoir layer PET – Inlet layer Medical tape – channel layer

Figure C-5. On-body evaluation of the microfluidic flexible sensor patches for carbagel-based iontophoretic sweat stimulation and sampling at rest.

Timestamps represent the period (min) after a 5-min iontophoresis session. Black dye was used in the reservoir to facilitate the direct visualization of sweat flow in the microfluidics. Scale bars, 3 mm

Note C-1. Iontophoresis-based localized sweat stimulation

Iontophoresis is a common procedure that enables on-demand sweat induction by transdermal delivery of a muscarinic agonist that stimulates sweat gland to produce sweat. Despite its widespread use in cystic fibrosis diagnosis, the choice of agonists is still mostly limited to pilocarpine and acetylcholine, which only affect the sweat glands where the agonist is delivered. Here we use carbachol, a muscarinic agonist that has nicotinic effects, which enable the sudomotor axon reflex sweating (SAR) and sweat glands neighboring the dosed area also produce sweat for sampling (Fig.

4-8b).¹ Carbachol is a cholinomimetic ester more resistant to acetylcholinesterase hydrolysis than acetylcholine and enables a prolonged sweat production time. Using a commercial iontophoresis device, we compared the sweat rate stimulated by commercial pilogels loaded with pilocarpine and custom made carbagels (Fig. 4-9),

Supplementary Fig. 31 Carbachol iontophoresis.

Trial 1 in the main figure ???

Scale 3 mm

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both with the same geometry and the same dosing area (a circle with a 27 mm diameter). Using the same commercial sweat collectors and the same sampling area (a concentric circle with a 28.4 mm diameter), the total sweat rates (of the dosed area and the surrounding area) of three subjects induced by carbagels is much higher and lasts longer than those induced by commercial pilogels (Fig. 4-8c). Moreover, with the same dosing area (a 11 mm diameter circle) blocked (by a 13 mm diameter adhesive disk) and the same commercial sweat collector (a concentric circle with a 28.4 mm diameter), the carbagels elicited significant SAR sweat rates in 3 subjects compared to none by pilogels (Fig. 4-8d). To avoid the potential contamination from gel, we harvest only the SAR sweat and the high sweat rate obtained is sufficient for continuous chemical sensing (Appendix C, Fig. C-5).

Bibliography of Appendix C

1. Simmers, P., Li, S. K., Kasting, G. & Heikenfeld, J. Prolonged and localized sweat stimulation by iontophoretic delivery of the slowly-metabolized cholinergic agent carbachol. Journal of Dermatological Science 89, 40–51 (2018).

C h a p t e r 5

INTEGRATED MICROFLUIDIC SWEAT SENSOR FOR METABOLIC MONITORING

Materials from this chapter appears in “Yang, Y.; Song, Y.; Bo, X.; Min, J.; Pak, O.

S.; Zhu, L.; Wang, M.; Tu, J.; Kogan, A.; Zhang, H.; Hsiai, T. K.; Li, Z.; Gao, W. A laser-engraved wearable sensor for sensitive detection of uric acid and tyrosine in sweat. Nature Biotechnology 38, 217–224 (2020) doi:10.1038/s41587-019-0321-x”

and “Wang, M.; Yang, Y.; Min, J.; Song, Y.; Tu, J.; Mukasa, D.; Ye, C.; Xu, C.;

5.1 Introduction

Wearable devices^1,2, such as wearable sweat sensors^3–5, have the potential to capture changes in health rapidly, continuously, and non-invasively. In order to be suitable for wearable use, system-level integration is necessary for the development of wearable sweat sensors. In Chapter 5.2, our efforts on system integration evaluations are presented.

Sweat contains rich molecular information for probing personal health condition.⁶ For example, chloride concentration in sweat is the gold standard to diagnose cystic fibrosis⁷, and glucose concentration in sweat is being intensively explored for diabetes management. ^5,7,8 In Chapter 5.3, our integrated microfluidic sweat sensing device is detailed for in situ monitoring towards metabolic health monitoring, with a focus on gout and metabolic syndrome management.

5.2 Wearable System Development and Validation 5.2.1 Wearable System Integration

The flexible and disposable sensor patch consists of carbachol-loaded iontophoresis electrodes, a multi-inlet microfluidic module, a multiplexed MIP nutrient sensor array, a temperature sensor, and an electrolyte sensor (Fig. 5-1). The sensor patch can be easily attached to skin with conformal contact and interfaces with a miniaturized electronic module in the form of a FPCB for on-demand iontophoresis control, in situ signal processing and wireless communication with the user interfaces through Bluetooth (Fig. 5-1g and Appendix D, Fig. D-1,D-2).

Figure 5-1. Schematics and images of the biomimetic wearable biosensor

‘NutriTrek’. a, Circulating nutrients such as amino acids are associated with various physiological and metabolic conditions. b, Schematic of the wearable ‘NutriTrek’

that enables metabolic monitoring through a synergistic fusion of laser-engraved graphene, redox-active nanoreporters, and biomimetic ‘artificial antibodies’. c,d, Schematic (c) and layer assembly (d) of the microfluidic ‘NutriTrek’ patch for sweat induction, sampling, and biosensing. T, temperature; PI, polyimide. e,f, Images of a

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flexible sensor patch (e) and a skin-interfaced wearable system (f). Scale bars, 5 mm (e) and 2 cm (f). g, Block diagram of electronic system of ‘NutriTrek’. The modules outlined in red dashes are included in the smartwatch version. ADC, analog-to-digital converter; DAC, digital-to-analog converter; CPU, central processing unit; GPIO, general-purpose input/output; POT, potentiometry; In-Amp, instrumentation amplifier; MCU, microcontroller; SPI, serial peripheral interface; TIA, transimpedance amplifier; UART, universal asynchronous receiver-transmitter. h, Custom mobile application for real-time metabolic and nutritional tracking. i,

‘NutriTrek’ smartwatch with a disposable sensor patch and an electrophoretic display. Scale bars, 1 cm (top) and 5 cm (bottom).

The block diagram of the electronic system (Fig. 5-1g and Appendix D, Fig. D-2) represents both the wearable electronic patch and the smart watch that can (i) induce sweat via iontophoresis and (ii) monitor sweat via electrochemical methods. The sweat induction and the sweat sensing procedures are initiated and controlled by the microcontroller when it receives a user command from the Bluetooth module. For sweat induction, programmable iontophoretic current is generated by a voltage controlled current source. For the optimized design, a 100-µA current (~2.6 µA mm^-

2) was applied for on-body iontophoresis sweat induction using the flexible microfluidic patch, with current output check and protection circuit (Appendix D, Note D-1). For sweat sensing, the voltammetry involves controlled voltage potentials between the electrodes. A series voltage reference and a digital to analog converter (DAC) is used to generate a dynamic potential bias across the reference and working electrodes. A bipotentiostat circuit is constructed by a control amplifier and two transimpedance amplifiers. An instrumentation amplifier is used for potentiometric measurements and a voltage divider is used for the resistive temperature sensor. All analog voltage signals are acquired by the microcontroller’s built-in analog-to-digital converter (ADC) channels, processed, then transmitted over Bluetooth to a user device. A custom mobile app ‘NutriTrek’ was developed to process, display, and store the dynamic metabolic information monitored by the

wearable sensors (Fig. 5-1h). The wearable system was also integrated into a smartwatch with an electronic paper display (Fig. 5-1i).

While on-board signal conditioning, processing and wireless transmission provides feasible scheme for wearable sensing, in situ sweat analysis poses more factors to consider to achieve accurate sensing outcome due to complex and interpersonally varied sweat composition and demands technological innovations for accurate on- body sensing, unlike classic bioaffinity sensors which operate in optimal buffers. For example, for direct LEG-MIP Trp sensing, a DPV scan in sweat even before target/MIP recognition could lead to an oxidation peak as a small amount of electroactive molecules (e.g., Trp and Tyr) can be oxidized on the surface of MIP layer; after recognition and binding of Trp into the MIP cavities, a substantially higher current peak height can be obtained; measuring difference of the two peak heights allows more accurate bound Trp measurement directly in sweat with high selectivity (Fig. 5-2a-c).

Figure 5-2. The two-scan sensor calibration strategy enabling selective Trp sensing in situ in the presence of Tyr. a, scheme of the two-scan strategy. b, peak height currents directly from the DPV scan before and after recognition incubation time. c, peak height difference caused by target recognition gives consistent sensing outcome in the presence of different Tyr levels. ∆I, peak height current; ∆I’, peak height difference caused by target recognition.

Moreover, the sensing outcome is influenced by temperature and ionic strength (Fig.

5-3), so real-time readings from an LEG-based strain-resistive temperature sensor and an ion-selective Na⁺ sensor can be used to calibrate the LEG-MIP reading(Fig.

5-4). Considering that sweat rate during exercise was reported to have influence on certain biomarker levels; we could use sweat Na⁺ level (which showed a linear correlation with sweat rate) to further calibrate the nutrient levels for personalized analysis. This unique transduction strategy involving both the two-step DPV scans and the temperature/electrolyte calibrations allows us to obtain accurate reading continuously in sweat during on-body use (Fig. 5-5).

Figure 5-3. The performance of the LEG-MIP sensor under varied temperature and electrolyte levels. a, Color map showing the dependence of the LEG-MIP Trp sensor response on Trp and Na⁺ concentrations. b, Open circuit potential responses of an LEG-based Na⁺ sensor in the presence of varied Na⁺ concentrations. Inset, calibration plot of an LEG-based Na⁺ sensor. c, Color map showing the dependence of the LEG-MIP Trp sensor response on Trp and temperature. d, Calibration plot of an LEG-based temperature sensor in the physiological temperature range. Solid calibration lines in b,d represent linear fit trendlines.

Supplementary Fig. 26 LEG-MIP sensor calibration against temperature and electrolyte levels. a, Color map showing the dependence of the LEG- MIP Trp sensor response on Trp and Na+ concentrations. b, The open circuit potential responses of a LEG-based Na+ sensor in the presence of varied Na+ concentrations. Inset, calibration plot of the LEG-based Na+ sensor. c, Color map showing the dependence of the LEG-MIP Trp sensor response on Trp and temperature. d, Dynamic response of an LEG-based temperature sensor in the physiological temperature range. Inset, calibration plot of the LEG-based temperature sensor.

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Figure 5-4. Mutiplexed sensing for calibrating MIP sensing outcome. a, Illustration of a multifunctional wearable sensor patch. b, Electrolyte calibration of the AA sensor reading.

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Supplementary Fig. 27 In situcalibration strategies of wearable LEG-MIP sensor involving a two-step DPV scan calibration and real-time temperature/electrolyte calibrations. a,b, In situ calibration strategies of the MIP-LEG sensor with direct detection mechanism (a) and the MIP-RAR- LEG sensor with indirect detection mechanism (b) to obtain accurate reading continuously during on-body use.

b V DPV

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Figure 5-5. In situ calibration strategies of the wearable LEG-MIP sensors involving a two-step DPV-scan calibration and real-time temperature/electrolyte calibrations. (A and B) In situ calibration strategies of the MIP-LEG sensor with direct detection mechanism (a) and the MIP-RAR (AQCA used here for wearable sensing)-LEG sensor with indirect detection mechanism (b) to obtain accurate reading continuously during on-body use.

5.2.2 System Evaluation in Human Subjects

Evaluation of the wearable system was conducted first via sensing of sweat Trp and Tyr in human subjects during a constant-load cycling exercise trial (Fig. 5-6a–d and Appendix D, Fig. D-3). The DPV data from the sensors were wirelessly transmitted along with temperature and Na⁺ sensor readings to the mobile app that automatically extracted the oxidation peaks using a custom developed iterative baseline correction algorithm (Fig. 5-6e and Appendix D, Fig. D-4) and performed calibration for the accurate quantification of sweat Tyr and Trp.

Figure 5-6. Wearable system evaluation across activities toward prolonged physiological and nutritional monitoring. a–d, Continuous on-body Trp and Tyr analysis using a wearable sensor array with real-time sensor calibrations during cycling exercise. e, Custom voltammogram analysis with an automatic peak extraction strategy based on a polynomial fitting and cut-off procedure. f–j, Dynamic sweat Trp and BCAA analysis during physical exercise toward central fatigue monitoring. k–o, Dynamic analysis of sweat AA levels with and without Trp and Tyr supplement intake at rest toward personalized nutritional monitoring.

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Considering that AAs (e.g., Try and BCAAs) play a crucial role in central fatigue during physical exercise⁹, a flexible Trp and BCAA sensor array was used to monitor the AA dynamics during vigorous exercise (Fig. 5-6f–j and Fig. 5-7). Both Trp and BCAA levels decreased during the exercise due to the serotonin synthesis and BCAA ingestion, respectively. The increased sweat Trp/BCAA ratio was observed which could potentially serve as an indicator for central fatigue, in agreement with a previous report on its plasma counterpart⁹.

Figure 5-7. Dynamic monitoring of central fatigue using the Trp/BCAA sensor array patches. a–c, BCAA (a), Trp (b) and Trp/BCAA ratio (c) before exercise and after vigorous exercise until fatigue in human serum. d–f, BCAA (d), Trp (e) and Trp/BCAA ratio (f) before exercise and after vigorous exercise until fatigue in iontophoresis sweat.

The wearable iontophoresis-integrated patch enables daily continuous AA monitoring at rest beyond the physical exercise. As illustrated in Fig. 5-6k–o and Appendix D, Figs. D-5-7, rising Trp and Tyr levels in sweat were observed from all four subjects after Trp and Tyr supplement intake while the readings from the sensors remained stable during the studies without intake (Fig. 5-8). Such capability opens the door for personalized nutritional monitoring and management through

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Supplementary Fig. 36 Dynamic monitoring of central fatigue using Trp/BCAA sensor array patches. BCAA (a), Trp (b) and Trp/BCAA ratio (c) before exercise and after vigorous exercise until fatigue in serum. d–f, BCAA (a), Trp (b) and Trp/BCAA ratio (c) before exercise and after vigorous exercise until fatigue in iontophoresis sweat.

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